Overview

Dataset statistics

Number of variables12
Number of observations2825
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory286.9 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

gross_revenue is highly overall correlated with qtde_invoices and 3 other fieldsHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_itens is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly overall correlated with u_basket_sizeHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
u_basket_size is highly overall correlated with qtde_products and 1 other fieldsHigh correlation
avg_ticket is highly skewed (γ1 = 48.90445591)Skewed
frequency is highly skewed (γ1 = 22.8687173)Skewed
qtde_returns is highly skewed (γ1 = 50.55990893)Skewed
basket_size is highly skewed (γ1 = 45.1395063)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.2%) zerosZeros
avg_recency_days has 51 (1.8%) zerosZeros
qtde_returns has 1524 (53.9%) zerosZeros

Reproduction

Analysis started2023-11-10 00:09:48.612749
Analysis finished2023-11-10 00:10:49.894415
Duration1 minute and 1.28 second
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2825
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15299.886
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:50.375367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12626.2
Q113827
median15271
Q316801
95-th percentile17953.4
Maximum18287
Range5940
Interquartile range (IQR)2974

Descriptive statistics

Standard deviation1714.2532
Coefficient of variation (CV)0.11204352
Kurtosis-1.2048524
Mean15299.886
Median Absolute Deviation (MAD)1484
Skewness0.0021078867
Sum43222179
Variance2938664
MonotonicityNot monotonic
2023-11-09T21:10:50.969407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17615 1
 
< 0.1%
13220 1
 
< 0.1%
15985 1
 
< 0.1%
15172 1
 
< 0.1%
13692 1
 
< 0.1%
15745 1
 
< 0.1%
16639 1
 
< 0.1%
15491 1
 
< 0.1%
15265 1
 
< 0.1%
Other values (2815) 2815
99.6%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18265 1
< 0.1%
18263 1
< 0.1%
18261 1
< 0.1%
18260 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2809
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2879.4906
Minimum36.56
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:51.514080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum36.56
5-th percentile251.878
Q1614.66
median1142.99
Q32389.1
95-th percentile7538.114
Maximum279138.02
Range279101.46
Interquartile range (IQR)1774.44

Descriptive statistics

Standard deviation10856.745
Coefficient of variation (CV)3.77037
Kurtosis334.71234
Mean2879.4906
Median Absolute Deviation (MAD)683.75
Skewness16.301075
Sum8134560.9
Variance1.1786891 × 108
MonotonicityNot monotonic
2023-11-09T21:10:52.133127image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
731.9 2
 
0.1%
1025.44 2
 
0.1%
1314.45 2
 
0.1%
1353.74 2
 
0.1%
379.65 2
 
0.1%
734.94 2
 
0.1%
598.2 2
 
0.1%
889.93 2
 
0.1%
745.06 2
 
0.1%
178.96 2
 
0.1%
Other values (2799) 2805
99.3%
ValueCountFrequency (%)
36.56 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
70.02 1
< 0.1%
77.4 1
< 0.1%
84.65 1
< 0.1%
90.3 1
< 0.1%
93.35 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

ZEROS 

Distinct257
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.084956
Minimum0
Maximum373
Zeros34
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:52.693701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q110
median29
Q374
95-th percentile215
Maximum373
Range373
Interquartile range (IQR)64

Descriptive statistics

Standard deviation70.211083
Coefficient of variation (CV)1.2087654
Kurtosis3.2630081
Mean58.084956
Median Absolute Deviation (MAD)24
Skewness1.8725457
Sum164090
Variance4929.5962
MonotonicityNot monotonic
2023-11-09T21:10:53.210866image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.5%
4 87
 
3.1%
2 86
 
3.0%
3 85
 
3.0%
8 76
 
2.7%
10 69
 
2.4%
9 66
 
2.3%
7 65
 
2.3%
17 62
 
2.2%
22 56
 
2.0%
Other values (247) 2074
73.4%
ValueCountFrequency (%)
0 34
 
1.2%
1 99
3.5%
2 86
3.0%
3 85
3.0%
4 87
3.1%
5 43
1.5%
7 65
2.3%
8 76
2.7%
9 66
2.3%
10 69
2.4%
ValueCountFrequency (%)
373 1
 
< 0.1%
372 1
 
< 0.1%
369 1
 
< 0.1%
366 1
 
< 0.1%
360 1
 
< 0.1%
358 3
0.1%
354 1
 
< 0.1%
337 1
 
< 0.1%
336 2
0.1%
334 1
 
< 0.1%

qtde_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9823009
Minimum2
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:53.899959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q36
95-th percentile17
Maximum206
Range204
Interquartile range (IQR)4

Descriptive statistics

Standard deviation9.0045848
Coefficient of variation (CV)1.5052043
Kurtosis186.42448
Mean5.9823009
Median Absolute Deviation (MAD)2
Skewness10.689372
Sum16900
Variance81.082548
MonotonicityNot monotonic
2023-11-09T21:10:54.591076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 826
29.2%
3 503
17.8%
4 394
13.9%
5 237
 
8.4%
6 173
 
6.1%
7 138
 
4.9%
8 98
 
3.5%
9 69
 
2.4%
10 55
 
1.9%
11 54
 
1.9%
Other values (45) 278
 
9.8%
ValueCountFrequency (%)
2 826
29.2%
3 503
17.8%
4 394
13.9%
5 237
 
8.4%
6 173
 
6.1%
7 138
 
4.9%
8 98
 
3.5%
9 69
 
2.4%
10 55
 
1.9%
11 54
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_itens
Real number (ℝ)

HIGH CORRELATION 

Distinct1654
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1676.8634
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:55.299905image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile114.2
Q1323
median684
Q31469
95-th percentile4588.4
Maximum196844
Range196842
Interquartile range (IQR)1146

Descriptive statistics

Standard deviation6027.3154
Coefficient of variation (CV)3.5943987
Kurtosis445.02325
Mean1676.8634
Median Absolute Deviation (MAD)442
Skewness17.461199
Sum4737139
Variance36328531
MonotonicityNot monotonic
2023-11-09T21:10:55.984955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 8
 
0.3%
246 8
 
0.3%
219 7
 
0.2%
200 7
 
0.2%
394 7
 
0.2%
493 7
 
0.2%
1200 7
 
0.2%
516 7
 
0.2%
300 7
 
0.2%
Other values (1644) 2749
97.3%
ValueCountFrequency (%)
2 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
27 2
0.1%
30 1
< 0.1%
32 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

qtde_products
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.19044
Minimum2
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:56.646904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q134
median71
Q3142
95-th percentile393.6
Maximum7838
Range7836
Interquartile range (IQR)108

Descriptive statistics

Standard deviation275.55026
Coefficient of variation (CV)2.1495382
Kurtosis341.98106
Mean128.19044
Median Absolute Deviation (MAD)44
Skewness15.457136
Sum362138
Variance75927.945
MonotonicityNot monotonic
2023-11-09T21:10:57.379958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 39
 
1.4%
35 36
 
1.3%
27 31
 
1.1%
26 31
 
1.1%
29 30
 
1.1%
31 28
 
1.0%
25 28
 
1.0%
15 28
 
1.0%
19 27
 
1.0%
33 26
 
0.9%
Other values (457) 2521
89.2%
ValueCountFrequency (%)
2 11
0.4%
3 14
0.5%
4 17
0.6%
5 16
0.6%
6 26
0.9%
7 15
0.5%
8 14
0.5%
9 20
0.7%
10 19
0.7%
11 23
0.8%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1945
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.841717
Minimum2.15
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:58.018887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.762
Q112.22
median17.89
Q324.98
95-th percentile88.1
Maximum56157.5
Range56155.35
Interquartile range (IQR)12.76

Descriptive statistics

Standard deviation1090.5299
Coefficient of variation (CV)19.185379
Kurtosis2490.1285
Mean56.841717
Median Absolute Deviation (MAD)6.41
Skewness48.904456
Sum160577.85
Variance1189255.4
MonotonicityNot monotonic
2023-11-09T21:10:58.587929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.49 7
 
0.2%
16.82 6
 
0.2%
16.53 6
 
0.2%
17.66 6
 
0.2%
19.06 6
 
0.2%
17.13 5
 
0.2%
10 5
 
0.2%
19.44 5
 
0.2%
16.92 5
 
0.2%
20.75 5
 
0.2%
Other values (1935) 2769
98.0%
ValueCountFrequency (%)
2.15 1
< 0.1%
2.43 1
< 0.1%
2.46 1
< 0.1%
2.51 1
< 0.1%
2.52 1
< 0.1%
2.65 1
< 0.1%
2.66 1
< 0.1%
2.71 1
< 0.1%
2.76 1
< 0.1%
2.77 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
13305.5 1
< 0.1%
4453.43 1
< 0.1%
1687.2 1
< 0.1%
1377.08 1
< 0.1%
952.99 1
< 0.1%
872.13 1
< 0.1%
841.02 1
< 0.1%
651.17 1
< 0.1%
640 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1218
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.125451
Minimum0
Maximum366
Zeros51
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:10:59.237581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.5568627
Q130
median54
Q392.666667
95-th percentile212.6
Maximum366
Range366
Interquartile range (IQR)62.666667

Descriptive statistics

Standard deviation65.559622
Coefficient of variation (CV)0.89653631
Kurtosis4.1293141
Mean73.125451
Median Absolute Deviation (MAD)28.714286
Skewness1.9103653
Sum206579.4
Variance4298.0641
MonotonicityNot monotonic
2023-11-09T21:10:59.926632image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
1.8%
70 20
 
0.7%
31 20
 
0.7%
14 16
 
0.6%
21 16
 
0.6%
55 15
 
0.5%
46 15
 
0.5%
42 15
 
0.5%
49 14
 
0.5%
25 13
 
0.5%
Other values (1208) 2630
93.1%
ValueCountFrequency (%)
0 51
1.8%
0.0303030303 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3333333333 1
 
< 0.1%
0.8571428571 1
 
< 0.1%
1 8
 
0.3%
1.5 1
 
< 0.1%
1.819512195 1
 
< 0.1%
1.878787879 1
 
< 0.1%
2 3
 
0.1%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
364 1
 
< 0.1%
363 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1226
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087031574
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:11:00.613769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088183697
Q10.015873016
median0.024793388
Q30.043478261
95-th percentile0.14935245
Maximum17
Range16.99455
Interquartile range (IQR)0.027605245

Descriptive statistics

Standard deviation0.436269
Coefficient of variation (CV)5.012767
Kurtosis810.59065
Mean0.087031574
Median Absolute Deviation (MAD)0.011000285
Skewness22.868717
Sum245.8642
Variance0.19033064
MonotonicityNot monotonic
2023-11-09T21:11:01.281816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 47
 
1.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.6%
0.09090909091 15
 
0.5%
0.08333333333 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.02564102564 13
 
0.5%
0.02127659574 13
 
0.5%
Other values (1216) 2643
93.6%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
4 1
 
< 0.1%
3 5
 
0.2%
2 47
1.7%
1.142857143 1
 
< 0.1%
1 8
 
0.3%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%

qtde_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct205
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.067611
Minimum0
Maximum80995
Zeros1524
Zeros (%)53.9%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:11:02.000418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile94.8
Maximum80995
Range80995
Interquartile range (IQR)8

Descriptive statistics

Standard deviation1550.2251
Coefficient of variation (CV)24.580368
Kurtosis2633.7647
Mean63.067611
Median Absolute Deviation (MAD)0
Skewness50.559909
Sum178166
Variance2403197.8
MonotonicityNot monotonic
2023-11-09T21:11:02.669731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1524
53.9%
1 131
 
4.6%
2 118
 
4.2%
3 82
 
2.9%
4 72
 
2.5%
6 63
 
2.2%
5 56
 
2.0%
12 46
 
1.6%
8 39
 
1.4%
9 38
 
1.3%
Other values (195) 656
23.2%
ValueCountFrequency (%)
0 1524
53.9%
1 131
 
4.6%
2 118
 
4.2%
3 82
 
2.9%
4 72
 
2.5%
5 56
 
2.0%
6 63
 
2.2%
7 38
 
1.3%
8 39
 
1.4%
9 38
 
1.3%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1950
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.69986
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:11:03.353811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.044
Q1102
median171.08
Q3277
95-th percentile586.9
Maximum40498.5
Range40497.5
Interquartile range (IQR)175

Descriptive statistics

Standard deviation801.56855
Coefficient of variation (CV)3.2757214
Kurtosis2255.2607
Mean244.69986
Median Absolute Deviation (MAD)81.42
Skewness45.139506
Sum691277.1
Variance642512.15
MonotonicityNot monotonic
2023-11-09T21:11:03.931851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
86 9
 
0.3%
75 8
 
0.3%
60 8
 
0.3%
197 7
 
0.2%
82 7
 
0.2%
73 7
 
0.2%
136 7
 
0.2%
208 7
 
0.2%
105 7
 
0.2%
Other values (1940) 2747
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
3.33 1
< 0.1%
5.33 1
< 0.1%
5.67 1
< 0.1%
6.14 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
11.88 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.33 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2733.94 1
< 0.1%
2518.77 1
< 0.1%
2160.33 1
< 0.1%
2082.23 1
< 0.1%
2000 1
< 0.1%
1903.5 1
< 0.1%

u_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct982
Distinct (%)34.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.101989
Minimum1
Maximum299.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.1 KiB
2023-11-09T21:11:04.537747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.382
Q110.09
median17.25
Q328
95-th percentile56.664
Maximum299.71
Range298.71
Interquartile range (IQR)17.91

Descriptive statistics

Standard deviation18.851079
Coefficient of variation (CV)0.85291323
Kurtosis23.858004
Mean22.101989
Median Absolute Deviation (MAD)8.25
Skewness3.1328747
Sum62438.12
Variance355.36318
MonotonicityNot monotonic
2023-11-09T21:11:05.184799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 45
 
1.6%
14 31
 
1.1%
11 31
 
1.1%
1 27
 
1.0%
9 27
 
1.0%
7.5 27
 
1.0%
17.5 26
 
0.9%
10.5 26
 
0.9%
17 24
 
0.8%
15.5 24
 
0.8%
Other values (972) 2537
89.8%
ValueCountFrequency (%)
1 27
1.0%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.33 2
 
0.1%
1.5 8
 
0.3%
1.57 2
 
0.1%
1.67 4
 
0.1%
1.83 1
 
< 0.1%
2 22
0.8%
2.05 1
 
< 0.1%
ValueCountFrequency (%)
299.71 1
< 0.1%
203.5 1
< 0.1%
145 1
< 0.1%
136.12 1
< 0.1%
135.5 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%
114 1
< 0.1%
110.33 1
< 0.1%
110 1
< 0.1%

Interactions

2023-11-09T21:10:43.176672image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:49.305334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:53.421979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:58.624989image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:04.392015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:09.232368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:13.901321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:18.347353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:22.299206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:27.267980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:32.604467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:37.828961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:43.598704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:49.546200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:53.723599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:59.071723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:04.843884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:09.556615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:14.240946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:18.668375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:22.567228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:27.724894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:33.056534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:38.087382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:44.035801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:49.794374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:54.041975image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:59.576557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:05.231192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:10.164711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:14.589356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:19.020360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:22.978125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:28.165919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:33.516696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:38.424407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:44.492719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:50.094920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:54.508242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:59.959795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:05.662394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:10.579065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:14.980125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:19.314381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:23.468156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:28.624992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:34.020095image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:38.895589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:44.971540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:50.314830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:55.014979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:00.473776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:06.100822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:11.075640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:15.308227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:19.648434image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:23.920645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:29.056094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:34.505398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:39.413631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:45.456136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:50.539302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:55.484388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:00.941150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:06.504318image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:11.427952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:15.650107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:19.991462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:24.200730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:29.543131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:35.008487image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:39.961871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:45.951171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:50.920324image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:55.949842image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:01.449737image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:06.894608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:11.783760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:15.976856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:20.305481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:24.842734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:30.059166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:35.494526image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:40.480910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:46.874296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:51.224290image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:56.576481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:01.909285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:07.256236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:12.081996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:16.266919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:20.667542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:25.113240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:30.474168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:35.949348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:40.951953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:47.347330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:51.523782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:56.908016image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:02.395940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:07.693666image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:12.441274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:16.506675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:21.010562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:25.459313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:30.864191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:36.470382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:41.476511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:47.792363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:51.946920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:57.292819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:02.932837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:08.055169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:12.821583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:17.082198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:21.298583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:25.917339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:31.308228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:36.779421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:41.827540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:48.211916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:52.441149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:57.723705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:03.466186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:08.482112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:13.179622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:17.582235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:21.653614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:26.380419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:31.786818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:37.186902image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:42.202563image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:48.685949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:52.942500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:09:58.240365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:03.977145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:08.910306image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:13.609354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:18.007329image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:22.015637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:26.839443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:32.276425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:37.515929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-09T21:10:42.696640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-09T21:11:05.674877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
customer_id1.000-0.0940.0140.003-0.0850.007-0.146-0.0310.027-0.062-0.1220.006
gross_revenue-0.0941.000-0.3800.7620.9200.7180.284-0.3410.2130.4640.6040.278
recency_days0.014-0.3801.000-0.452-0.373-0.3970.0310.186-0.095-0.190-0.112-0.110
qtde_invoices0.0030.762-0.4521.0000.7040.6590.099-0.4530.2720.4300.1330.019
qtde_itens-0.0850.920-0.3730.7041.0000.7060.203-0.3160.1980.4260.7640.310
qtde_products0.0070.718-0.3970.6590.7061.000-0.375-0.2870.1700.3260.4050.723
avg_ticket-0.1460.2840.0310.0990.203-0.3751.000-0.0680.0620.1970.203-0.623
avg_recency_days-0.031-0.3410.186-0.453-0.316-0.287-0.0681.000-0.971-0.215-0.0260.061
frequency0.0270.213-0.0950.2720.1980.1700.062-0.9711.0000.1510.007-0.071
qtde_returns-0.0620.464-0.1900.4300.4260.3260.197-0.2150.1511.0000.2120.021
basket_size-0.1220.604-0.1120.1330.7640.4050.203-0.0260.0070.2121.0000.430
u_basket_size0.0060.278-0.1100.0190.3100.723-0.6230.061-0.0710.0210.4301.000

Missing values

2023-11-09T21:10:49.103509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-09T21:10:49.609558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
0178505391.21372.034.01733.0297.018.150.03030317.00000040.050.978.74
1130473232.5956.09.01390.0171.018.9039.6250000.02830235.0154.4419.00
2125836705.382.015.05028.0232.028.9026.5000000.04032350.0335.2015.47
313748948.2595.05.0439.028.033.8769.5000000.0179210.087.805.60
415100876.00333.03.080.03.0292.0020.0000000.07317122.026.671.00
5152914623.3025.014.02102.0102.045.3326.7692310.04011529.0150.147.29
6146885630.877.021.03621.0327.017.2218.3000000.057221399.0172.4315.57
7178095411.9116.012.02057.061.088.7232.4545450.03352041.0171.425.08
81531160767.900.091.038194.02379.025.544.1444440.243316474.0419.7126.14
9160982005.6387.07.0613.067.029.9347.6666670.0243900.087.579.57
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itensqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsbasket_sizeu_basket_size
563817468137.0010.02.0116.05.027.404.0000000.4000000.058.002.5
564913596697.045.02.0406.0166.04.207.0000000.2500000.0203.0083.0
5655148931237.859.02.0799.073.016.962.0000000.6666670.0399.5036.5
565717852114.3411.02.053.024.04.760.0000002.0000000.026.5012.0
567417772182.7710.02.058.053.03.450.0000002.0000000.029.0026.5
568014126706.137.03.0508.015.047.081.5000000.75000050.0169.335.0
568116479300.8310.02.0102.035.08.600.0000002.0000000.051.0017.5
5686135211092.391.03.0733.0435.02.514.5000000.3000000.0244.33145.0
569615060301.848.04.0262.0120.02.520.3333332.0000000.065.5030.0
57661600012393.702.03.05110.09.01377.080.0000003.0000000.01703.333.0